Temporal motion vector filtering

ABSTRACT

Methods and apparatus, including computer program products, implementing and using techniques for performing temporal motion vector filtering in digital video are disclosed. A recursive hierarchical process is used to determine a motion vector. In the recursive hierarchical process, a neighborhood of old motion vectors is filtered to generate a first estimated motion vector for an image patch in a pair of two image frames. The filtering process uses multiple vectors in a neighborhood around the old motion vector to improve the prediction of the first estimated motion vector. The temporal vector partitioning process separates motion vectors associated with an object from motion vectors associated with a background before selecting a best motion vector, which improves the selection process. The process also works well in the absence of object/background boundaries, as in this case the outlier (incorrect) vector or vectors will be separated out from the good vectors.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims benefit of priority from U.S. Provisional PatentApplication No. 60/532,435, filed Dec. 23, 2003, and entitled “TEMPORALVECTOR PARTITIONING,” which is hereby incorporated by reference herein.

BACKGROUND

This invention relates to improving video and graphics processing.

At low display refresh rates (for example, 50 fields/sec for interlacedvideo material, and 24 frames/sec for film-originated material) onprogressive digital display devices, a display artifact referred to as“area flicker” can occur. The area flicker becomes more visible as thesize of the display increases, due to the high sensitivity to flicker inthe human visual peripheral region. A simple solution for reducing thearea flicker is to increase the display refresh rate by repeating theinput fields or frames at a higher rate (for example, 100 fields/sec forinterlaced video). This solves the area flicker problem for staticscenes. However, the repetition introduces a new artifact in scenes withmotion, known as “motion judder” or “motion smear,” particularly inareas with high contrast, due to the human eye's tendency to track thetrajectory of moving objects. For this reason, motion compensated frameinterpolation is preferred, in which the pixels are computed in aninterpolated frame or field at an intermediate point on a local motiontrajectory, so that there is no discrepancy between an expected imagemotion due to eye tracking and a displayed image motion. The local imagemotion trajectory from one field or frame to the next is described by amotion vector.

Motion vectors can be computed at different levels of spatialresolution, such as at a pixel level, at an image patch level, or at anobject level. Computing a motion vector for every pixel independentlywould theoretically result in an ideal data set, but is unfeasible dueto the large number of computations required. Computing a motion vectorfor each image patch reduces the number of computations, but can resultin artifacts due to motion vector discontinuities within an image patch.Computing motion vectors on an object basis can theoretically result inhigh resolution and lower computational requirements, but objectsegmentation is a challenging problem.

Therefore what is needed is a way to determine motion vectorsefficiently and accurately, such that little or no discrepancy existsbetween an expected image motion due to eye tracking and a displayedimage motion in a digital video.

SUMMARY

The present invention provides methods and apparatus for determiningmotion vectors efficiently and accurately, such that little or nodiscrepancy exists between an expected image motion due to eye trackingand a displayed image motion in a digital video.

In general, in one aspect, the invention provides methods and apparatus,including computer program products, implementing and using techniquesfor performing temporal motion vector filtering in a digital videosequence. Several vectors are received, the vectors representingpotential motion vectors for an image patch including one or more of anobject and a background. The vectors are partitioned into two or morevector clusters. A representative vector is determined for each vectorcluster. Each representative vector is tested to determine whichrepresentative vector most accurately reflects a displacement of theimage patch between a first frame and a second frame of the digitalvideo. The representative vector that most accurately reflects thedisplacement of the image patch is selected as a motion vector.

Advantageous implementations can include one or more of the followingfeatures. Partitioning can include determining a first seed vector for afirst cluster and a second seed vector for a second cluster byidentifying two vectors among the vectors that are furthest apart fromeach other, and for every other vector, placing the vector into thefirst cluster if the vector is closest to the first seed vector, andplacing the vector into the second cluster if the vector is closest tothe second seed vector. Determining a representative vector can includefor each cluster, determining which vector in the cluster has a minimumtotal distance from all the other vectors in the cluster. Each clustercan represent an object or a background in the digital video. Each imagepatch can include several pixels. One vector can represent an old motionvector originating at a first pixel and ending at a second pixel, andthe other vectors originate at the first pixel and end at pixelsdifferent from the second pixel in a horizontal direction or a verticaldirection. The size of each image patch can be 8 by 8 pixels.

Testing each representative vector can include, for each representativevector, centering a first window on a pixel that forms an origin of therepresentative vector, centering a second window on a pixel that formsan end point of the representative vector, determining a sum of absolutedifferences of luma values for the pixels in the first window and pixelsat corresponding positions in the second window, and selecting as therepresentative vector which most accurately reflects a displacement ofthe image patch, the representative vector that has a minimum sum ofabsolute differences. The dimensions of the first and second windows canbe identical to the dimensions of the image patch.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flowchart of a recursive hierarchical process fordetermining a motion vector.

FIG. 2 shows an example of vectors for determining a best motion vectorat a resolution of 1:4 of an original resolution of a video frame.

FIG. 3 shows an example of vectors for determining a best motion vectorat a resolution of 1:2 of an original resolution of a video frame.

FIG. 4 shows an example of vectors for determining a best motion vectorat an original resolution of a video frame.

FIG. 5A shows a flowchart of a process for performing temporal vectorpartitioning.

FIG. 5B shows an exemplary set of neighborhood vectors that can be usedin a temporal vector partitioning process.

FIG. 6 illustrates a computer system employed to implement theinvention.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides methods and apparatus for determiningmotion vectors efficiently and accurately, such that little or nodiscrepancy exists between an expected image motion due to eye trackingand a displayed image motion in a digital video. This is accomplished byusing a recursive hierarchical approach including a temporal vectorpartitioning scheme to determine motion vectors.

Generally, for motion compensated approaches to work well, including therecursive hierarchical approach described herein, two basic assumptionsare made about the nature of the object motion: 1) moving objects haveinertia, and 2) moving objects are large. The inertia assumption impliesthat a motion vector changes only gradually with respect to a temporalvector sampling interval (that is, the frame rate in the digital video).The large objects assumption implies that a motion vector changes onlygradually with respect to a spatial vector sampling interval, that is,the vector field is smooth and has only few boundary motiondiscontinuities.

The goal of the recursive hierarchical method is to find a motion vectorby applying a source correlation window to a first image frame and atarget correlation window to a subsequent image frame, and placing thetarget correlation window such that a best match with the sourcecorrelation window is obtained, that is, the contents of the sourcecorrelation window and target correlation window are as similar aspossible. At the same time, the number of calculations needed to performthe matching between the source correlation window and the targetcorrelation window must be as low as possible, while still searching theentire vector space limit. In order to accomplish these goals, therecursive hierarchical method uses multiple resolution levels of theimage frames. A best motion vector is first determined for the lowestresolution level by projecting the previous best motion vector at thehighest resolution level down to the lowest resolution level, andtesting it and one or more updates. This best motion vector is thenpropagated up to a higher resolution level, where some adjustments aremade and a new best motion vector is determined. This new best motionvector is propagated up to yet another higher resolution level, wheremore adjustments are made and another new best motion vector isdetermined. This process is repeated until the highest, original,resolution level has been reached and a best motion vector has beenidentified for the original resolution level.

FIG. 1 shows one implementation of a recursive hierarchical process(100). It is assumed that multiple resolution levels of the image frameshave already been generated. As can be seen in FIG. 1, the recursivehierarchical process (100) for determining a motion vector starts byprojecting a motion vector from a previous image frame down to a lowestresolution level (step 102). A set of update vectors is generated andtested to find a best motion vector at this lowest resolution level(step 104). In one implementation this test is performed by comparingpixels in corresponding positions in a source correlation windowcentered on the origin of the motion vector and a target correlationwindow centered on the end point of each respective update vector. Thecomparison can, for example, be performed by subtracting a luma valuefor each pixel in the source window from the corresponding pixel in therespective target windows. In this case the best match would be definedby finding a minimum sum of absolute differences (SAD) for a sourcecorrelation window and a target correlation window pair, and the bestmotion vector would be the vector associated with this sourcecorrelation window and a target correlation window pair.

After the minimum SAD has been found, the best vector is selected (step106). The process (100) then examines whether there are any higherresolution levels (step 108). If there are higher resolution levels, theprocess propagates the best vector up to the next higher resolutionlevel (step 110) and repeats steps 104 through 108. If there are nohigher resolution levels, the process proceeds to step 112, where thebest vector is selected as the motion vector and is used for motioncompensation, which completes the process for the current frame.

The advantage of this approach is that at a lower level, an update of apixel is equivalent to an update of two or more pixels at the nexthigher level, depending on the difference in resolution between the twolevels. If there are, for example, three resolution levels, say 1:1, 1:2and 1:4, and an update of +/−1 pixel at each level, the convergencedelay is potentially reduced by a factor of four. Expressed differently,effectively the resolution hierarchy is used to accelerate the temporalrecursion convergence. This results in significant improvements, inparticular for frames containing small objects moving with highvelocities.

The invention will now be explained in greater detail, by way of exampleof a recursive hierarchical scheme with three levels of resolution at1:1, 1:2, and 1:4, with an image patch grid of 4×4 pixels, and withreference to FIGS. 1-4. It should be noted that the vectors shown inFIGS. 2-4 are representative only of this example, and that the numberof resolution levels and the number and/or the types of vectors at eachresolution level can be varied depending on various factors, such ascomputational cost, quality, processing speed, and so on.

FIG. 4 shows an image patch grid (400), which is divided into imagepatches (405) of 4×4 pixels, where each pixel is illustrated as a circle(410). The dark pixels (415) indicate locations at which motion vectorsare computed for each 4×4 image patch of pixels. As can be seen in FIG.4, one motion vector is computed for each 4×4 image patch of pixels andthe locations within each 4×4 image patch of the motion vectors' originsare the same. FIG. 3 shows the same pixel grid (400) at half theresolution of the original pixel grid of FIG. 4. FIG. 2 shows the samepixel grid (400) at the lowest resolution, which in the present exampleis half the resolution of FIG. 3, or a fourth of the resolution of FIG.4.

As shown in FIG. 1 and FIG. 2, a recursive hierarchical process fordetermining a motion vector starts by projecting a motion vector (205)from a previous image down to a lowest resolution level (step 102),which in the present example is 1:4 of the original resolution, and isillustrated in FIG. 2. In one implementation the old motion vector (205)is filtered before it is projected, primarily to take care of cases inwhich the neighborhood contains an object-background boundary thatcauses a discontinuity in the vectors. This process is also referred toas temporal vector partitioning and will be explained in further detailbelow. The filtered output is a new base vector at the 1:1 level, whichis subsequently projected down to 1:4 level. In the first frame of asequence, that is, when there is no previous image, the process (100)starts with a zero vector as the old motion vector. In oneimplementation, the zero vector is also used when there is a scene breakin the video, that is, when there is no continuity between two frames.

FIG. 5A shows one implementation of a temporal vector partitioningprocess (500). As was described above, the purpose of the temporalvector partitioning process (500) is to give a better estimation of anold motion vector (205) to be projected to the lowest resolution level,as shown in FIG. 2. Therefore, rather than simply projecting a singlemotion vector, a neighborhood (550) including multiple vectors, as shownin FIG. 5B, is examined. Furthermore, it is assumed that theneighborhood of vectors (550) includes an object/background boundary.The temporal vector partitioning process (500) attempts to separatemotion vectors associated with an object from motion vectors associatedwith a background before selecting a best motion vector, which furtherimproves the selection process.

As can be seen in FIG. 5A, the process (500) starts with obtaining a setof neighborhood vectors (550) from a previous image frame. The set (550)shown in FIG. 5B includes nine vectors, each pointing to an image patch(560). In the present example, nine adjacent image patches (560) areused to define the neighborhood vectors, but only five of them (V1through V5, arranged in an X-shaped pattern) are used for thecomputations in the implementation described herein. However, the readershould realize that any number of vectors can be selected, and that theneighborhood can have many different shapes. The set of fiveneighborhood vectors and a square shaped set of image patches (560) areonly used herein for exemplary purposes.

The process then partitions the set of neighborhood vectors (550) intotwo clusters (step 504). In one implementation, the partitioning isperformed by determining which two vectors are furthest apart from eachother and using these two vectors as seeds for the two clusters. Afterthe two seed vectors for the clusters have been determined, eachremaining vector is sorted into one of the two clusters, based on whichcluster seed vector they are closest to.

Next, the process determines a representative vector for each cluster(step 506). The purpose of determining representative vectors is to findexisting vectors that are the best representatives for the respectiveclusters. In one implementation, the representative vectors aredetermined as the vectors that have the minimum distances from all theother vectors in their respective cluster. The minimum distance can, forexample, be calculated by determining a distance from each vector in acluster to all other vectors in the same cluster and adding thedistances. The vector with the minimum total distance is selected as therepresentative vector.

When the two representative vectors have been found, the processdetermines which representative vector provides a best match when theimage patch is moved the distance and direction defined by therespective representative vector (step 508). This can, for example, bedone by using two correlation windows, where one correlation window iscentered around the origin of the vector, and the other is centeredaround the end point of the vector, and determining a minimum sum ofabsolute differences (SAD) for the pixels in the two correlationwindows. Exactly how this is done will be described in further detailbelow, but for the purposes of FIG. 5A, the important result is that abest match is found for one of the two representative vectors. Theprocess then selects as a candidate vector the representative vectorthat has the best match (step 510). The selected vector is subsequentlyprojected down to the lowest resolution level, and the process ends. Thebest match vector represents the object vector, and the other vectorrepresents a background vector.

The partitioning described above helps in resolving object/backgroundvector discontinuities around smaller boundary details, such as a hoodornament on a moving car. The partitioning also works equally well onneighborhoods that do not contain any object boundaries, since most ofthe vectors will be in one cluster and the other cluster will justcontain one or a few “outlier” vectors.

Returning now to FIGS. 1 and 2, after the filtered vector has beenprojected to the lowest resolution level, a set of update vectors (210a-210 f) is generated and tested to find a minimum SAD at +/−1 pixel or+/−2 pixels from the old filtered projected motion vector (step 104). InFIG. 2, six update vectors (210 a-210 f) are illustrated, two for +/−1pixel and two for +/−2 pixels in the horizontal direction, and two for+/−1 pixel in the vertical direction, since horizontal movement isgenerally greater than vertical movement. However, as the reader skilledin the art will realize, any number of update vectors can be generatedand tested at any horizontal and/or vertical location in relation to theprojected vector (205). In one implementation, a predicted camera vectoris also projected down to 1:4 level. The camera vector will be discussedin further detail below.

In one implementation, the SAD is computed by letting the candidatevectors for an image patch, which all originate at the same image patchlocation in the source frame, point to different pixel locations in atarget frame. For each candidate vector, a rectangular window iscentered in the target frame on the pixel pointed to by the respectivecandidate vector. A corresponding rectangular window is centered in thesource frame on the pixel where the candidate vectors originate. Then apair wise absolute difference of the corresponding luma pixels in thetwo windows, that is, the pixels that have the same relative locationwithin the two windows, is calculated. The sum of all the absolutedifferences is the SAD value. The SAD decreases as the window matchingbecomes better and is ideally zero when the pixels are identical. Inpractice, of course, due to noise and other factors, the best vectorwill have a non-zero SAD, but will have the minimum SAD of the vectorsin the set of candidate vectors.

After the minimum SAD has been found the best vector, that is, thevector with the minimum SAD (210 f) is selected and stored in memory(step 106). The process then examines whether there are any higherresolution levels (step 108). As was described above, in this examplethere are two higher resolution levels, so the process propagates thebest vector (210 f) is projected up to the 1:2 resolution level shown inFIG. 3 (step 110). Again, a set of update vectors (305 a-305 d) isgenerated around the best vector (210 f) after it has been projected upto the 1:2 level (step 104). At this level, a second set of updatevectors (310 a-310 d) is also generated around the old 1:1 filteredvector (205) projected down to the 1:2 resolution level. A new bestvector (305 a) is found, by computing the minimum SAD among all theupdate vectors, just like on the 1:4 resolution level. The best updatevector is then selected and is stored in memory (step 106).

The process then examines again whether there are any higher resolutionlevels (step 108). At this point, there is one higher resolution levelleft in the resolution pyramid, so the process returns again to step104, where the best vector (305 a) from the 1:2 resolution level in FIG.3 is filtered and projected up to the highest 1:1 resolution level shownin FIG. 4. Again, a set of update vectors (405 a-405 d) is generatedaround the projected and filtered best vector (305 a) (step 104). Atthis level, a second set of update vectors (410 a-410 d) is alsogenerated around the old 1:1 filtered vector. A third set of updatevectors (420 a-420 d) is generated around a camera vector (415).

The camera vector describes a global movement of the contents of theframe, as opposed to the local vectors at each image patch location thatare computed completely independently, and can therefore be used to aidin finding a better true motion vector. In several commonly occurringscenarios a motion vector resulting from camera movements at everylocation in a frame can be predicted quite easily with a simple model.For example, in the case of a camera lens panning across a distantlandscape, all the motion vectors will be identical and equivalent tothe velocity of the camera. Another scenario is when a camera lens zoomsinto an object on a flat surface, such as a picture on a wall. All themotion vectors then have a radial direction and increase from zero atthe image center to a maximum value at the image periphery.

In one implementation, the process tries to fit a mathematical model tothe motion vectors that have been computed using a least squares method.A good fit between the camera motion vectors and the mathematical modelindicates that one of the scenarios discussed above likely is present,and the camera model predicted vector can then be used as an additionalcandidate vector in the next recursive hierarchical vector estimationstep. Taking the camera vector into consideration is advantageous inthat the recursive portion of the recursive hierarchical search is alocal search approach, which may converge into a false local minimuminstead of the true minimum. The camera predicted vector candidate canpotentially help in avoiding detection of false local minima and directthe process towards a true minimum.

The new best vector (405 d) is then found, just like on the 1:4 and 1:2resolution levels (step 106) and is stored in memory. The process thenexamines again whether there are any higher resolution levels available(step 108). This time there are no higher resolution levels, so theprocess proceeds to step 112, where the best vector is selected and usedfor motion compensation, which completes the process for the currentframe.

The above process is performed for all the 4×4 image patches of pixelsin the frame, and based on the determined motion vectors, aninterpolation of frames between a source frame and a target frame can bemade, so that there is a minimal or no discrepancy between an expectedimage motion due to eye-tracking, and a displayed image motion.

As can be seen from the above discussion, the invention provides asmooth and accurate vector field and uses only a fairly small number ofcalculations. Furthermore, there is reduced convergence delay since dueto the multiple levels of resolution. Fewer resolution levels can beused compared to conventional approaches, and vector errors in lowerlevels are not amplified due to resolution changes at higher resolutionlevels due to safeguarding by testing projected vectors at eachresolution. Performing a temporal vector partitioning during thefiltering of a motion vector determined for a previous image pair canhelp with resolving object-background vector discontinuities aroundsmaller boundary details, for example, a hood ornament on a moving car,or similar types of details. At the same time the temporal vectorpartitioning does not adversely affect image areas that do not containobject boundaries. In this scenario the outlier vector (i.e., theincorrect vector) or vectors will be separated out from the goodvectors, and so the procedure will still be of benefit.

The invention can be implemented in digital electronic circuitry, or incomputer hardware, firmware, software, or in combinations of them.Apparatus of the invention can be implemented in a computer programproduct tangibly embodied in a machine-readable storage device forexecution by a programmable processor; and method steps of the inventioncan be performed by a programmable processor executing a program ofinstructions to perform functions of the invention by operating on inputdata and generating output. The invention can be implementedadvantageously in one or more computer programs that are executable on aprogrammable system including at least one programmable processorcoupled to receive data and instructions from, and to transmit data andinstructions to, a data storage system, at least one input device, andat least one output device. Each computer program can be implemented ina high-level procedural or object-oriented programming language, or inassembly or machine language if desired; and in any case, the languagecan be a compiled or interpreted language. Suitable processors include,by way of example, both general and special purpose microprocessors.Generally, a processor will receive instructions and data from aread-only memory and/or a random access memory. Generally, a computerwill include one or more mass storage devices for storing data files;such devices include magnetic disks, such as internal hard disks andremovable disks; magneto-optical disks; and optical disks. Storagedevices suitable for tangibly embodying computer program instructionsand data include all forms of non-volatile memory, including by way ofexample semiconductor memory devices, such as EPROM, EEPROM, and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM disks. Any of the foregoing canbe supplemented by, or incorporated in, ASICs (application-specificintegrated circuits).

FIG. 6 shows a computer system (600) employed to implement theinvention. The computer system (600) is only an example of a graphicssystem in which the present invention can be implemented. The computersystem (600 includes a central processing unit (CPU) (610), a randomaccess memory (RAM)

-   -   (620), a read only memory (ROM) (625), one or more peripherals        (630), a graphics controller (660), primary storage devices (640        and 650), and a digital display unit (670). As is well known in        the art, ROM acts to transfer data and instructions        uni-directionally to the CPUs (610), while the RAM (620) is used        typically to transfer data and instructions in a bi-directional        manner. The CPUs (610) can generally include any number of        processors. Both primary storage devices (640 and 650) can        include any suitable computer-readable media. A secondary        storage medium (680), which is typically a mass memory device,        is also coupled bi-directionally to the CPUs (610) and provides        additional data storage capacity. The mass memory device (680)        is a computer-readable medium that can be used to store programs        including computer code, data, and the like. Typically, the mass        memory device (680) is a storage medium such as a hard disk or a        tape which generally slower than the primary storage devices        (640, 650). The mass memory storage device (680) can take the        form of a magnetic or paper tape reader or some other well-known        device. It will be appreciated that the information retained        within the mass memory device (680), can, in appropriate cases,        be incorporated in standard fashion as part of the RAM (620) as        virtual memory.

The CPUs (610) are also coupled to one or more input/output devices(690) that can include, but are not limited to, devices such as videomonitors, track balls, mice, keyboards, microphones, touch-sensitivedisplays, transducer card readers, magnetic or paper tape readers,tablets, styluses, voice or handwriting recognizers, or other well-knowninput devices such as, of course, other computers. Finally, the CPUs(610) optionally can be coupled to a computer or telecommunicationsnetwork, e.g., an Internet network or an intranet network, using anetwork connection as shown generally at (695). With such a networkconnection, it is contemplated that the CPUs (610) might receiveinformation from the network, or might output information to the networkin the course of performing the above-described method steps. Suchinformation, which is often represented as a sequence of instructions tobe executed using the CPUs (610), can be received from and outputted tothe network, for example, in the form of a computer data signal embodiedin a carrier wave. The above-described devices and materials will befamiliar to those of skill in the computer hardware and software arts.

The graphics controller (660) generates image data and a correspondingreference signal, and provides both to digital display unit (670). Theimage data can be generated, for example, based on pixel data receivedfrom the CPU (610) or from an external encode (not shown). In oneembodiment, the image data is provided in RGB format and the referencesignal includes the VSYNC and HSYNC signals well known in the art.However, it should be understood that the present invention can beimplemented with data and/or reference signals in other formats. Forexample, image data can include video signal data also with acorresponding time reference signal.

A number of implementations of the invention have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the invention. Forexample in addition to the hierarchical and temporal vectors in theintermediate layers, the camera model generated vector projected downcan also be used as a candidate for SAD computation. Accordingly, otherembodiments are within the scope of the following claims.

1. A method for performing temporal motion vector filtering in a digitalvideo sequence, comprising: receiving a plurality of vectors, thevectors representing potential motion vectors for an image patchincluding one or more of an object and a background; partitioning theplurality of vectors into two or more vector clusters; determining arepresentative vector for each vector cluster; testing eachrepresentative vector to determine which representative vector mostaccurately reflects a displacement of the image patch between a firstframe and a second frame of the digital video; and selecting as a motionvector the representative vector that most accurately reflects thedisplacement of the image patch.
 2. The method of claim 1, whereinpartitioning includes: determining a first seed vector for a firstcluster and a second seed vector for a second cluster by identifying twovectors among the plurality of vectors that are furthest apart from eachother; and for each other vector in the plurality of vectors: placingthe vector into the first cluster if the vector is closest to the firstseed vector; and placing the vector into the second cluster if thevector is closest to the second seed vector.
 3. The method of claim 1,wherein determining a representative vector includes: for each cluster,determining which vector in the cluster has a minimum total distancefrom all the other vectors in the cluster.
 4. The method of claim 1,wherein each cluster represents an object or a background in the digitalvideo.
 5. The method of claim 1, wherein each image patch includes aplurality of pixels.
 6. The method of claim 1, wherein one vector in theplurality of vectors represents an old motion vector originating at afirst pixel and ending at a second pixel, and the other vectors in theplurality of vectors originate at the first pixel and end at pixelsdifferent from the second pixel in a horizontal direction or a verticaldirection.
 7. The method of claim 1, wherein the size of each imagepatch is 8 by 8 pixels.
 8. The method of claim 1, wherein testing eachrepresentative vector includes: for each representative vector:centering a first window on a pixel that forms an origin of therepresentative vector; centering a second window on a pixel that formsan end point of the representative vector; determining a sum of absolutedifferences of luma values for the pixels in the first window and pixelsat corresponding positions in the second window; and selecting as therepresentative vector most accurately reflects a displacement of theimage patch the representative vector that has a minimum sum of absolutedifferences.
 9. The method of claim 8, wherein the dimensions of thefirst and second windows are identical to the dimensions of the imagepatch.
 10. A computer program product, stored on a machine-readablemedium, comprising instructions operable to cause a computer to: receivea plurality of vectors, the vectors representing potential motionvectors for an image patch including one or more of an object and abackground; partition the plurality of vectors into two or more vectorclusters; determine a representative vector for each vector cluster;test each representative vector to determine which representative vectormost accurately reflects a displacement of the image patch between afirst frame and a second frame of the digital video; and select as amotion vector the representative vector that most accurately reflectsthe displacement of the image patch.
 11. The computer program product ofclaim 10, wherein the instructions to partition include instructions to:determine a first seed vector for a first cluster and a second seedvector for a second cluster by identifying two vectors among theplurality of vectors that are furthest apart from each other; and foreach other vector in the plurality of vectors: place the vector into thefirst cluster if the vector is closest to the first seed vector; andplace the vector into the second cluster if the vector is closest to thesecond seed vector.
 12. The computer program product of claim 10,wherein the instructions to determine a representative vector includeinstructions to: for each cluster, determine which vector in the clusterhas a minimum total distance from all the other vectors in the cluster.13. The computer program product of claim 10, wherein each clusterrepresents an object or a background in the digital video.
 14. Thecomputer program product of claim 10, wherein each image patch includesa plurality of pixels.
 15. The computer program product of claim 10,wherein one vector in the plurality of vectors represents an old motionvector originating at a first pixel and ending at a second pixel, andthe other vectors in the plurality of vectors originate at the firstpixel and end at pixels different from the second pixel in a horizontaldirection or a vertical direction.
 16. The computer program product ofclaim 10, wherein the size of each image patch is 8 by 8 pixels.
 17. Thecomputer program product of claim 10, wherein the instructions to testeach representative vector include instructions to: for eachrepresentative vector: center a first window on a pixel that forms anorigin of the representative vector; center a second window on a pixelthat forms an end point of the representative vector; determine a sum ofabsolute differences of luma values for the pixels in the first windowand pixels at corresponding positions in the second window; and selectas the representative vector most accurately reflects a displacement ofthe image patch the representative vector that has a minimum sum ofabsolute differences.
 18. The computer program product of claim 17,wherein the dimensions of the first and second windows are identical tothe dimensions of the image patch.